Predicting Motion Plans for Articulating Everyday Objects
Predicting Motion Plans for Articulating Everyday Objects
复制标题
DOI：
10.1109/icra48891.2023.10160752
复制
发表时间：
2023-03
期刊：
影响因子：
--
通讯作者：
Arjun Gupta;Max E. Shepherd;Saurabh Gupta
中科院分区：
文献类型：
--
作者：
Arjun Gupta;Max E. Shepherd;Saurabh Gupta
移动操作任务，如开门、拉开抽屉或抬起马桶座圈，需要末端执行器在环境和任务约束下的受限运动。这一点，再加上新环境中的部分信息，使得在测试时采用经典的运动规划方法具有挑战性。我们的关键见解是将其作为一个学习问题，以利用过去解决类似计划问题的经验，直接预测在测试时间的新情况下移动操作任务的运动计划。为了实现这一点，我们开发了一个模拟器，ArtObjSim，模拟放置在真实场景中的铰接对象。然后我们引入$\mathbf{SeqIK}+\theta_{0}$，一个快速灵活的运动计划表示。最后，我们学习使用$\mathbf{SeqIK}+\theta_{0}$的模型，以快速预测在测试时表达新对象的运动计划。实验评估表明，在生成运动计划的速度和准确性方面，纯基于搜索的方法和纯学习的方法都有所提高。
Mobile manipulation tasks such as opening a door, pulling open a drawer, or lifting a toilet seat require constrained motion of the end-effector under environmental and task constraints. This, coupled with partial information in novel environments, makes it challenging to employ classical motion planning approaches at test time. Our key insight is to cast it as a learning problem to leverage past experience of solving similar planning problems to directly predict motion plans for mobile manipulation tasks in novel situations at test time. To enable this, we develop a simulator, ArtObjSim, that simulates articulated objects placed in real scenes. We then introduce $\mathbf{SeqIK}+\theta_{0}$, a fast and flexible representation for motion plans. Finally, we learn models that use $\mathbf{SeqIK}+\theta_{0}$ to quickly predict motion plans for articulating novel objects at test time. Experimental evaluation shows improved speed and accuracy at generating motion plans than pure search-based methods and pure learning methods.